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Real-world constraints of production AI

Differences between pilots and production: stability, cost, governance

Pixel Quest: Production AI Fundamentals

Key roles in production: who does what (AI Ops, MLOps, LLMOps) explained

Resource limits: budgets, people, data quality, and risk

Stakeholders: product teams, security, privacy, and legal

Decisions gates: when to deploy, rollback, or pause

What good looks like in early production: small, measurable wins

From project to operations: bridging gaps

Idea to value chain: turning business need into operable AI

Artifacts for ops: runbooks, dashboards, incident templates

Data governance basics: access, privacy, retention

Incident response: detect, triage, and rollback steps

Safety, guardrails, and policy alignment

Creating portfolio-ready projects for early-career roles

First-year checklist: learning goals and early projects

Knowledge Check

Quiz: Operational Roles and Responsibilities

Quiz: Managing Resources and Constraints

Quiz: Decision Gates and Deployment

Quiz: Incident Response and Governance

Quiz: Operational Artifacts and Safety

Course Feedback

Course Survey

Operational AI: Mastering the Transition from Pilot to Production is a practical, step-by-step course for teams and professionals who need to run AI reliably in the real world. Move beyond demos and sandboxes to master stability, governance, and cost control as you deploy, monitor, and evolve AI systems at scale. Through clear frameworks, hands-on artifacts, and scenario-based decision gates, you’ll learn how to convert a promising prototype into a resilient, compliant, and cost-effective service. Explore the differences between pilots and production, define who does what across AIOps, MLOps, and LLMOps, and build the operational muscle needed for 24/7 AI. You’ll create runbooks, dashboards, and incident workflows; map business value to technical SLIs/SLOs; and implement safety guardrails that align with security, privacy, and legal. Designed for early-career operators, product-minded data scientists, and engineers expanding into operations, this course emphasizes small, measurable wins and portfolio-ready projects. By the end, you’ll be able to set clear go/no-go criteria, manage rollbacks confidently, monitor drift and performance, and communicate risk with stakeholders. If you’re ready to make AI dependable for users—and defensible for your org—this course is your launchpad.

Operational AI: Mastering the Transition from Pilot to Production

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            <p>The transition from an experimental AI pilot to a robust production environment represents one of the most significant hurdles in modern enterprise technology. While initial prototypes often focus on model accuracy and proof-of-concept, operationalizing AI requires a rigorous focus on stability, cost-efficiency, and comprehensive governance frameworks. This course provides a structured exploration of the real-world constraints that define production AI, moving beyond the theoretical to address the practicalities of resource management, data quality, and the mitigation of operational risks. By examining the critical differences between experimental code and production-ready systems, participants will gain the foundational knowledge necessary to navigate the complexities of deploying AI at scale.</p>

<p>Designed for students, early-career professionals, and corporate staff, this curriculum bridges the gap between technical development and sustainable operations. Participants will explore the distinct responsibilities of AIOps, MLOps, and LLMOps roles, while learning to collaborate effectively with cross-functional stakeholders in security, privacy, and legal departments. The course further details the essential artifacts of operational excellence—such as runbooks, incident response templates, and monitoring dashboards—and establishes clear decision gates for deployment and rollback. Ultimately, this program equips learners with a strategic roadmap for their first year in the field, offering guidance on building portfolio-ready projects and aligning technical outputs with measurable business value.</p>
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